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2b6c6b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import torch
class RexLR(torch.optim.lr_scheduler.LRScheduler):
"""
Reflected Exponential (REX) learning rate scheduler (https://arxiv.org/abs/2107.04197)
Modified from: https://github.com/IvanVassi/REX_LR (Apache-2.0 License)
Args:
optimizer (torch.optim.Optimizer): The optimizer to schedule the learning rate for
max_lr (float): The maximum learning rate
min_lr (float): The minimum learning rate
num_steps (int): The total number of training steps
num_warmup_steps (int): The number of warmup steps
rex_alpha (float): Constant added to the denominator of the REX factor;
prevents division-by-zero and softens the initial decay (default: 0.1).
rex_beta (float): Multiplier of z in the denominator of the REX factor;
controls how quickly the decay flattens as z increases (default: 0.9).
last_epoch (int): The index of the last step
"""
def __init__(self, optimizer, max_lr, min_lr=0.0, num_steps=0, num_warmup_steps=0, rex_alpha=0.1, rex_beta=0.9, last_epoch=-1):
if min_lr > max_lr:
raise ValueError(f'Value of "min_lr" should be less than value of "max_lr". Got min_lr={min_lr} and max_lr={max_lr}')
if num_warmup_steps > num_steps:
raise ValueError(f"num_warmup_steps ({num_warmup_steps}) must be less than or equal to num_steps ({num_steps})")
self.min_lr = min_lr
self.max_lr = max_lr
self.num_steps = num_steps
self.num_warmup_steps = num_warmup_steps
self.rex_alpha = rex_alpha
self.rex_beta = rex_beta
self.last_epoch = last_epoch
# Ensure each parameter group has an "initial_lr" key to avoid issues when resuming
for group in optimizer.param_groups:
group.setdefault("initial_lr", group["lr"])
super().__init__(optimizer, last_epoch)
def get_lr(self):
# Single warmup step
if self.num_warmup_steps == 1 and self.last_epoch == 1:
return [self.min_lr for _ in self.base_lrs]
# Multiple warmup steps; increase lr linearly from min_lr to max_lr
elif self.num_warmup_steps > 1 and self.last_epoch >= 1 and self.last_epoch <= (self.num_warmup_steps - 1):
return [
self.min_lr + (self.max_lr - self.min_lr) * (self.last_epoch - 1) / (self.num_warmup_steps - 1)
for _ in self.base_lrs
]
# Post-warmup phase: adjust step relative to the end of warmup
step_after = self.last_epoch - self.num_warmup_steps
remaining_steps = self.num_steps - self.num_warmup_steps
# Avoid LR spiking
if step_after >= remaining_steps or step_after == -1 or remaining_steps <= 0:
return [self.min_lr for _ in self.base_lrs]
# Calculate REX curve for current step
rex_z = (remaining_steps - (step_after % remaining_steps)) / remaining_steps
rex_factor = self.min_lr / self.max_lr + (1.0 - self.min_lr / self.max_lr) * (
rex_z / (self.rex_alpha + self.rex_beta * rex_z)
)
return [base_lr * rex_factor for base_lr in self.base_lrs]
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